Crafting Captivating Podcasts with AI: A Step-by-Step Guide to Utilizing ElevenLabs and LlamaIndex

Creating a podcast from scratch can seem like a daunting task, but thanks to advancements in AI technology, the process has become significantly streamlined. This tutorial will guide you through generating a podcast episode from planning to production using AI tools such as LlamaIndex for document indexing and ElevenLabs for voice cloning and audio generation.

Introduction to LlamaIndex and ElevenLabs

LlamaIndex is a Python package designed for indexing and querying document data, which enhances the capabilities of language models like GPT-4 by providing them with context from a vast array of documents. ElevenLabs, on the other hand, brings to the table revolutionary voice cloning technology that can generate realistic audio from text input.

Setting Up Your Environment

Before diving into the podcast creation, you need to set up your environment. Ensure Python 3.7+ is installed on your system, and then install the necessary packages by running:

pip install llama-index elevenlabs

Indexing Documents with LlamaIndex

To begin with, gather the documents you want your podcast to reference. LlamaIndex can work with various data sources, including .txt files, PDFs, URLs, Google Docs, and Notion pages. If your documents are in a local directory, use the following code to index them:

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, \
StorageContext, load_index_from_storage
import os

def createVectorIndex(path):
    PERSIST_DIR = "./storage"
    if not os.path.exists(PERSIST_DIR):
        documents = SimpleDirectoryReader(path).load_data()
        index = VectorStoreIndex.from_documents(documents)
        index.storage_context.persist(persist_dir=PERSIST_DIR)
    else:
        storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
        index = load_index_from_storage(storage_context)
    return index

Calling `createVectorIndex(‘path/to/your/documents’)` will index your documents, storing the data locally for efficient querying.

Generating a Podcast Outline

With your documents indexed, the next step is to generate a podcast outline. Use LlamaIndex to query your document collection:

from llama_index.core import QueryEngine
import json

# Assume vectorIndex is your indexed data from the previous step
query_engine = vectorIndex.as_query_engine(similarity_top_k=10) 

prompt = """
Objective: Write a podcast episode outline using all the information given.

Podcast subject: <INSERT_PODCAST_SUBJECT>
Podcast cast: <INSERT_HOST_NAME> (host), <INSERT_HOST_NAME> (host), <INSERT_GUEST_NAME>(guest)
Podcast est runtime: 45 minutes 

return response in a json format that can be parsed using python `json.loads()`.
Example response format: 
{
    "outline": [
        {
            "topic": "<intro_topic>",   
            "documents_to_reference": [<doc1>, <doc2>]
        },
        {
            "topic": "<topic>",
            "documents_to_reference": [<doc1>, <doc2>]
        },
        {
            "topic": "<topic>",
            "documents_to_reference": [<doc1>, <doc2>]
        },
        {
            "topic": "<topic>",
            "documents_to_reference": [<doc1>, <doc2>]
        },
        {
            "topic": "<topic>",
            "documents_to_reference": [<doc1>, <doc2>]
        },
        {
            "topic": "<topic>",
            "documents_to_reference": [<doc1>, <doc2>]
        },
        {
            "topic": "<outro_topic>",
            "documents_to_reference": [<doc1>, <doc2>]
        }
    ]
}
"""

response = query_engine.query(prompt)
outline_response = json.loads(str(response))

This AI-generated outline serves as the foundation for your episode, allowing you to focus on creating content that covers all desired topics effectively.

Crafting Speaker Transcripts

For each section in your outline, you’ll need to generate detailed transcripts. To do this, create a prompt that specifies the podcast subject, cast, runtime, and documents to reference. Then, let a language model fill in the content:

transcript_prompt_template = """
Podcast Outline:
{outline}

Current Transcript:
{transcript}

Podcast subject: <INSERT_PODCAST_SUBJECT>
Podcast cast: <INSERT_HOST_NAME> (host), <INSERT_HOST_NAME> (host), <INSERT_GUEST_NAME>(guest)
Podcast est runtime: 45 minutes

Documents to reference during writing transcript:
{documents_to_reference}

Write the podcast transcript only for the topic "{topic}" and use as many speaker transcripts as needed."

return response in a json format that can be parsed using python `json.loads()`.
Example response format: 
{
    "transcripts": [
        {
            "speaker": "<speaker_name>",
            "transcript": "<transcript>"
        }
        ...
    ]
}
"""

full_transcript = []
for section in outline_response.get('outline'):
    ...
    transcript_response = query_engine.query(transcript_prompt)
    transcript_dict_response = json.loads(str(transcript_response))
    for speaker in transcript_dict_response.get('transcripts', []):
        full_transcript.append(speaker)

Generating Audio with ElevenLabs

Finally, it’s time to bring your podcast to life by creating audio clips for each transcript section using ElevenLabs’ voice cloning technology:

from elevenlabs import generate, Voice, VoiceSettings

speaker_name_to_voice_id = {
    '<INSERT_SPEAKER_NAME>': 'voice_id_here',
    ...
}

for speaker in full_transcript:
    audio = generate(
        api_key='<INSERT_API_KEY>',
        text=speaker.get('transcript'),
        voice=Voice(
            voice_id=speaker_name_to_voice_id[speaker.get('speaker')],
            ...
        )
    )
    ...

This process transforms the text transcripts into realistic audio segments, each voiced according to the designated speaker’s cloned voice.

Conclusion

By leveraging AI tools like LlamaIndex for document indexing and ElevenLabs for voice cloning and audio production, creating a podcast becomes a manageable, efficient process. The result is a professionally crafted episode, from outline to final audio, ready for publishing. Remember, while AI significantly streamlines content creation, human oversight is crucial to ensure quality and relevance. Happy podcasting!